Assistant Professor in the Statistics Department (by courtesy) at Purdue.

Lawson Building 2142-J, West Lafayette, IN 47907

e-mail: jhonorio at purdue.edu

Modern statistical problems are high dimensional (big data). My research in this area focus on developing computationally and statistically efficient algorithms, understanding their behavior using concepts such as convergence, sample complexity, and privacy, and designing new modeling paradigms such as models rooted in game theory. My theoretical and algorithmic work is directly motivated by, and contributes to, applications in political science (affiliation and influence), neuroscience (brain disorders such as addiction), and genetics (diseases such as cancer). [vita]

Prior to joining Purdue, I was a postdoctoral associate at MIT CSAIL, working with Tommi Jaakkola. My Erdös number is 3: Jean Honorio → Tommi Jaakkola → Noga Alon → Paul Erdös.

Here is a note for prospective students that are considering working with me.

CS 37300: Data Mining And Machine Learning: Fall 2018

CS 57800: Statistical Machine Learning: Spring 2018, also offered on Fall 2017 and Fall 2016

CS 69000-SML: Statistical Machine Learning II: Spring 2017

CS 52000: Computational Methods In Optimization: Spring 2016

Ghoshal A.,

On the Sample Complexity of Learning Graphical Games.

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.

Ghoshal A.,

From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria.

Ghoshal A.,

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data.

Barik A.,

(Under submission, 2018.)

Learning Causal Bayes Networks Using Interventional Path Queries in Polynomial Time and Sample Complexity. (Preprint)

Bello K.,

(Under submission, 2018.)

On the Statistical Efficiency of L

(Under submission, 2018.) [code]

Learning Linear Structural Equation Models in Polynomial Time and Sample Complexity.

Ghoshal A.,

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.

Ghoshal A.,

Information-Theoretic Limits of Bayesian Network Structure Learning.

Ghoshal A.,

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.

Variable Selection for Gaussian Graphical Models.

Lipschitz Parametrization of Probabilistic Graphical Models.

Multi-Task Learning of Gaussian Graphical Models.

Sparse and Locally Constant Gaussian Graphical Models.

Ke C.,

(Under submission, 2018.)

Information-Theoretic Lower Bounds for Recovery of Diffusion Network Structures.

Park K.,

Wang Z.,

(Under submission, 2018.)

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.

Li Y.,

(Under submission, 2018.)

On the Sample Complexity of Learning from a Sequence of Experiments. (Preprint)

Guo L.,

(Under submission, 2018.)

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. (Preprint)

Liu M.,

(Under submission, 2018.)

On the Statistical Efficiency of Compositional Nonparametric Prediction.

Xu Y.,

Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity.

Barik A.,

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.

A Unified Framework for Consistency of Regularized Loss Minimizers.

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.

Invited book chapter in

Edited by Aravkin A., Deng L., Heigold G., Jebara T., Kanevski D., Wright S. (to be published on December, 2016.)

Predictive Sparse Modeling of fMRI Data for Improved Classification, Regression, and Visualization Using the k-Support Norm.

Belilovsky E., Gkirtzou K., Misyrlis M., Konova A.,

Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise.

Invited book chapter in

Edited by Chen C.,

Improving Interpretability of Graphical Models in fMRI Analysis via Variable-Selection.

Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm.

Misyrlis M., Konova A., Blaschko M.,

Medical Image Computing and Computer-Assisted Intervention.

fMRI Analysis of Cocaine Addiction Using k-Support Sparsity.

Gkirtzou K.,

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.

Gkirtzou K.,

Medical Image Computing and Computer-Assisted Intervention,

Can a Single Brain Region Predict a Disorder?

Simple Fully Automated Group Classification on Brain fMRI.

Learning Brain fMRI Structure Through Sparseness and Local Constancy.

Neural Information Processing Systems,

A Functional Geometry of fMRI BOLD Signal Interactions.

Langs G., Samaras D., Paragios N.,

Neural Information Processing Systems,

Task-Specific Functional Brain Geometry from Model Maps.

Langs G., Samaras D., Paragios N.,

Moeller S.,

Dopaminergic Involvement During Mental Fatigue in Health and Cocaine Addiction.

Moeller S., Tomasi D.,

Enhanced Midbrain Response at 6-month Follow-up in Cocaine Addiction, Association with Reduced Drug-related Choice.

Moeller S., Tomasi D., Woicik P., Maloney T., Alia-Klein N.,

Dopaminergic contribution to endogenous motivation during cognitive control breakdown.

Moeller S., Tomasi D.,

Disrupted Functional Connectivity with Dopaminergic Midbrain in Cocaine Abusers.

Tomasi D., Volkow N., Wang R.,

Oral Methylphenidate Normalizes Cingulate Activity in Cocaine Addiction During a Salient Cognitive Task.

Goldstein R., Woicik P., Maloney T., Tomasi D., Alia-Klein N., Shan J.,

Dopaminergic Response to Drug Words in Cocaine Addiction.

Goldstein R., Tomasi D., Alia-Klein N.,

Anterior Cingulate Cortex Hypoactivations to an Emotionally Salient Task in Cocaine Addiction.

Goldstein R., Alia-Klein N., Tomasi D.,

Integration of Principal Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs.

Chen C., Gao G.,

Two-person Interaction Detection Using Body-Pose Features and Multiple Instance Learning.

Yun K.,

IEEE Computer Vision and Pattern Recognition,

Digital Analysis and Visualization of Swimming Motion.

Kirmizibayrak C.,

Digital Analysis and Visualization of Swimming Motion.

Kirmizibayrak C.,

Conference on Computer Animation and Social Agents,